Balancing energy consumption and risk of delay in high speed trains: A three-objective real-time eco-driving algorithm with fuzzy parameters

Eco-driving is an energy efficient traffic operation measure that may lead to important energy savings in high speed railway lines. When a delay arises in real time, it is necessary to recalculate an optimal driving that must be energy efficient and computationally efficient.In addition, it is important that the algorithm includes the existing uncertainty associated with the manual execution of the driving parameters and with the possible future traffic disturbances that could lead to new delays.This paper proposes a new algorithm to be executed in real time, which models the uncertainty in manual driving by means of fuzzy numbers. It is a multi-objective optimization algorithm that includes the classical objectives in literature, running time and energy consumption, and as well a newly defined objective, the risk of delay in arrival. The risk of delay in arrival measure is based on the evolution of the time margin of the train up to destination.The proposed approach is a dynamic algorithm designed to improve the computational time. The optimal Pareto front is continuously tracked during the train travel, and a new set of driving commands is selected and presented to the driver when a delay is detected.The algorithm evaluates the 3 objectives of each solution using a detailed simulator of high speed trains to ensure that solutions are realistic, accurate and applicable by the driver. The use of this algorithm provides energy savings and, in addition, it permits railway operators to balance energy consumption and risk of delays in arrival. This way, the energy performance of the system is improved without degrading the quality of the service.     

Urban single-lane roundabouts: A new analytical approach using multi-criteria and simultaneous multi-objective optimization of geometry design,efficiency and safety

Building safe and effective roundabouts requires optimizing traffic (operational) efficiency (TE) and traffic safety (TS) while taking into account geometric factors, traffic characteristics and local constraints. Most existing simulation-based optimization models do not simultaneously optimize all these factors. To capture the relationship among geometry, efficiency and safety, we put forward a model formulation in this paper. We present a new multi-criteria and simultaneous multi-objective optimization (MOO) model approach to optimize geometry, TE and TS of urban unsignalized single-lane roundabouts. To the best of our knowledge, this is the first model that uses the multi-criteria decision-making method known as analytic hierarchy process to evaluate and rank traffic parameters and geometric elements of urban single-lane roundabouts. The model was built based on comprehensive review of the research literature and existing roundabout simulation software, a field survey of 61 civil and traffic expert engineers in Croatia, and field studies of roundabouts in the Croatian capital city of Zagreb. We started from the basis of Kimber’s capacity model, HCM2010 serviceability model, and Maycock and Hall's accident prediction model, which we extended by adding sensitivity analysis and powerful MOO procedures of the bounded objective function method and interactive optimization. Preliminary validation of the model was achieved by identifying the optimal and most robust of three geometric alternatives (V.1-V.3) for an unsignalized single-lane roundabout in Zagreb, Croatia. The geometric parameters in variant V.1 had significantly higher values than in the existing design V.0, while approaches 1 and 3 in variant V.2 were enlarged as much as possible within allowed spatial limits and Croatian guidelines, reflecting their higher traffic demand. Sensitivity analysis indicated that variant V.2 showed the overall highest TE and TS across the entire range of traffic flow demand and pedestrian crossing flow demand at approaches. At the same time, the number of predicted traffic accidents was similar for all three variants, although it was lowest overall for V.2. The similarity in predicted accident frequency for the three variants suggests that V.2 provides the greatest safety within the predefined constraints and parameter ranges explored in our study. These preliminary results suggest that the proposed model can optimize geometry, TE and TS of urban single-lane roundabouts.     

Signal control optimization for automated vehicles at isolated signalized intersections

Traffic signals at intersections are an integral component of the existing transportation system and can significantly contribute to vehicular delay along urban streets. The current emphasis on the development of automated (i.e., driverless and with the ability to communicate with the infrastructure) vehicles brings at the forefront several questions related to the functionality and optimization of signal control in order to take advantage of automated vehicle capabilities. The objective of this research is to develop a signal control algorithm that allows for vehicle paths and signal control to be jointly optimized based on advanced communication technology between approaching vehicles and signal controller. The algorithm assumes that vehicle trajectories can be fully optimized, i.e., vehicles will follow the optimized paths specified by the signal controller. An optimization algorithm was developed assuming a simple intersection with two single-lane through approaches. A rolling horizon scheme was developed to implement the algorithm and to continually process newly arriving vehicles. The algorithm was coded in MATLAB and results were compared against traditional actuated signal control for a variety of demand scenarios. It was concluded that the proposed signal control optimization algorithm could reduce the ATTD by 16.2–36.9% and increase throughput by 2.7–20.2%, depending on the demand scenario.     

泥浆悬浮砂粒能力分层测定试验研究

为实现泥浆悬浮砂粒能力的量化分析,提出一种分层测定方法,选取12%、15%和18%3种质量分数的试验泥浆,通过自行设计的分层取样装置进行试验研究,获取不同泥浆质量分数、沉降时间和砂粒粒径条件下的10个深度分段的砂粒质量分布,分析各参数影响下的砂粒沉降变化规律。结果表明:1)该分层测定方法可较好地获取不同深度分段的砂粒质量,弥补现有测定方法只能获取底层砂粒沉降质量的不足;2)试验所采用的3种质量分数的泥浆对0.1 mm以上粒径的砂粒悬浮能力均较差。根据砂粒沉降曲线分布特征,提出一种用于判别泥浆悬浮砂粒能力的方法,即砂粒沉降曲线越平缓,各层质量差异性越小,底层质量无明显拐点,不同沉降时间曲线越紧密,则泥浆悬浮砂粒能力越强。     

基于响应面和遗传算法的柴油机瞬态过程喷油参数优化

基于台架试验数据,利用响应面法建立了某工程机械用柴油机瞬态过程喷油参数与性能的近似高精度模型,基于此模型采用遗传算法对瞬态过程喷油参数分别进行离线优化研究。结果表明:采用单目标优化确定的燃油消耗率(BSFC)、NO_x比排放量和颗粒质量(PM)比排放量的优化极限分别可达180.23g/(kW·h),8.92g/(kW·h)和0.011 8g/(kW·h),相对原机可降低多达4.5%,34.0%和37.3%。双目标优化的Pareto解集表明,相比于同时优化BSFC和NO_x比排放量,BSFC和PM比排放量更容易同时得到优化。采用权重因子适应度函数的三目标优化结果对应的BSFC,NO_x比排放量及PM比排放量分别为184.70g/(kW·h),12.62g/(kW·h)和0.012 2g/(kW·h),较原机分别降低2.1%,6.6%和35.3%。改进优化模型后,性能优化Pareto解集对应的BSFC和PM比排放量水平都非常接近其优化极限,但NO_x比排放量相对其优化极限仍然较高。     

基于小波和粒子群算法的HEV行驶状况辨识方法研究

针对混合动力汽车(HEV)行驶状况(道路坡度和整车载荷)变化难以有效识别,导致驱动系统控制策略不能有效满足驾驶员意图问题,以混联式HEV为研究对象,提出了基于小波滤波和粒子群算法的HEV行驶状况辨识方法。首先建立了汽车行驶状况辨识模型,采用最小二乘法确立了优化目标函数,其次研究了基于小波滤波和粒子群算法的HEV行驶状况辨识原理,最后进行了行驶状况粒子群智能算法辨识试验。在采集实车数据的基础上,对实车数据进行小波滤波,并运用行驶状况辨识方法对道路坡度和整车载荷进行了辨识,并对辨识结果进行小波滤波,结果表明,试验工况下整车载荷辨识的相对误差绝对平均值为2.71%,道路坡度辨识的相对误差绝对平均值为3.85%,验证了所提出方法的有效性。     

稀燃条件下甲醇汽油混合燃料颗粒物排放特性

在1台GDI增压汽油机上,进行了稀燃发动机燃用M0,M10和M20(其中M0为汽油,M10为甲醇体积分数10%、汽油体积分数90%的混合燃料,以此类推)甲醇汽油混合燃料的试验,研究了在稀燃条件下甲醇汽油混合燃料对GDI发动机颗粒粒径分布特性、数量浓度特性和质量浓度特性的影响。试验结果表明:在稀燃条件下,随着甲醇比例的增加颗粒数量浓度峰值逐渐增大,颗粒数量浓度随粒径分布呈现出双峰分布;颗粒中的核态颗粒和积聚态颗粒的总数量都随甲醇比例的增加而增加,其中M20的积聚态和核态颗粒数量浓度最大;颗粒粒径峰值都随着甲醇比例的增加逐渐增大,并且核态颗粒粒径峰值主要集中在20.54~31.62nm,积聚态颗粒粒径峰值主要集中在56.23~100nm;颗粒质量浓度随甲醇比例的增加而增大,粒径分布在316~700nm的积聚态颗粒明显增多,积聚态颗粒质量浓度随粒径分布的范围明显增加,质量浓度也相应增加,而粒径分布在56.23~316nm范围内的颗粒质量浓度却在降低。     

国Ⅴ柴油机SCR系统结构对尿素结晶影响研究

针对某国Ⅴ商用柴油车SCR系统中出现的结晶问题,综合应用CFD模拟和台架试验进行分析研究,提出一系列结构优化和改进方案,改善了SCR前流场状态以及流体的流动均匀性和尿素均匀性,基本消除了排气系统在实际工况下的结晶风险,提高了全系统的抗结晶能力,保证了SCR系统的正常运行。通过对模拟结果和台架试验数据的比对分析,研究了流场分布以及混合器结构对结晶形成产生的影响。     

动力总成悬置支架多目标拓扑优化和强度分析

基于Optistruct,以支架的最小柔度、最大固有频率作为优化目标函数;以应力、体积作为约束函数,对一发动机后拉杆安装支架进行拓扑优化,优化结果表明:在满足悬置支架强度和模态要求下,质量减少了12%。     

基于Moldflow的仪表面罩熔接线验证与结构优化

建立了分析模型,应用Moldflow软件对仪表面罩进行了注塑仿真分析。分析结果表明了熔接线位置,将其与实际零件熔接线缺陷进行了对比验证;同时从仪表面罩结构设计角度提出了可行性解决方案,并总结了在零件结构设计过程中导致熔接线缺陷的关键要素。对比验证结果表明:模流分析能有效指导优化仪表面罩结构,从而减少零件试模次数,提升零件质量,降低制造成本,缩短设计周期。